Abstract: A method and apparatus for rendering a volume sound source are disclosed. The method of rendering a volume sound source may include identifying information about a listener and information about the volume sound source, determining a corresponding area in which a source element is disposed in the volume sound source in consideration of the information about the listener, determining an angle between the listener and the corresponding area based on the information about the listener and the information about the volume sound source, determining a number of source elements disposed in the corresponding area according to the angle, determining a position and a gain of the source element using i) the number of source elements and ii) a distance between the listener and the volume sound source, and rendering the volume sound source according to the position and the gain of the source element.

Abstract: Disclosed are methods of encoding and decoding an audio signal using side information, and an encoder and a decoder for performing the methods. The method of encoding an audio signal using side information includes identifying an input signal, the input signal being an original audio signal, extracting side information from the input signal using a learning model trained to extract side information from a feature vector of the input signal, encoding the input signal, and generating a bitstream by combining the encoded input signal and the side information.

Abstract: Disclosed are a method for coding a residual signal of LPC coefficients based on collaborative quantization and a computing device for performing the method. The residual signal coding method includes: generating encoded LPC coefficients and LPC residual signals by performing LPC analysis and quantization on an input speech; Determining a predicted LPC residual signal by applying the LPC residual signal to cross module residual learning; Performing LPC synthesis using the coded LPC coefficients and the predicted LPC residual signal; It may include the step of determining an output speech that is a synthesized output according to a result of performing the LPC synthesis.

Abstract: Provided is an electrolyte solution for a lithium secondary battery, the electrolyte solution comprising a non-aqueous organic solvent, a lithium salt, and an additive, wherein the additive includes a compound represented by Chemical Formula 1. The contents of Chemical Formula 1 are the same as described in the specification.

Abstract: A communication device of a vehicle transmits a charging permission request message to a server and receives charging permission information from the server. A charging requester is configured to transmit a charging request message including the charging permission information to a charging device and a power receiver is configured to receive a charging response message from the charging device and to wirelessly receive power from the charging device.

Abstract: An audio signal encoding and decoding method using a neural network model, and an encoder and decoder for performing the same are disclosed. A method of encoding an audio signal using a neural network model, the method may include identifying an input signal, generating a quantized latent vector by inputting the input signal into a neural network model encoding the input signal, and generating a bitstream corresponding to the quantized latent vector, wherein the neural network model may include i) a feature extraction layer generating a latent vector by extracting a feature of the input signal, ii) a plurality of downsampling blocks downsampling the latent vector, and iii) a plurality of quantization blocks performing quantization of a downsampled latent vector.

Abstract: Provided are an apparatus and a method for integrally encoding and decoding a speech signal and a audio signal. The encoding apparatus may include: an input signal analyzer to analyze a characteristic of an input signal; a first conversion encoder to convert the input signal to a frequency domain signal, and to encode the input signal when the input signal is a audio characteristic signal; a Linear Predictive Coding (LPC) encoder to perform LPC encoding of the input signal when the input signal is a speech characteristic signal; and a bitstream generator to generate a bitstream using an output.

Abstract: Disclosed are a method of encoding a high band of an audio, a method of decoding a high band of an audio, and an encoder and a decoder for performing the methods. The method of decoding a high band of an audio, the method performed by a decoder, includes identifying a parameter extracted through a first neural network, identifying side information extracted through a second neural network, and restoring a high band of an audio by applying the parameter and the side information to a third neural network.

Abstract: Disclosed is a method and apparatus for label encoding in a multi-sound event interval. The method includes identifying an event interval in which a plurality of sound events occurs in a sound signal, separating a sound source into sound event signals corresponding to each sound event by performing sound source separation on the event interval, determining energy information for each of the sound event signals, and performing label encoding based on the energy information.

Abstract: A Unified Speech and Audio Codec (USAC) that may process a window sequence based on mode switching is provided. The USAC may perform encoding or decoding by overlapping between frames based on a folding point when mode switching occurs. The USAC may process different window sequences for each situation to perform encoding or decoding, and thereby may improve a coding efficiency.

Abstract: A packer classification apparatus extracts features based on a section that holds packer information from files and classifies packers using a Deep Neural Network(DNN) for detection of new/variant packers. A packer classification apparatus according to an embodiment uses PE section information. packer classification apparatus includes a collection classification module collecting a data set and classifying data by packer type to prepare for a model learning, a token hash module tokenizing a character string obtained after extracting labels and section names of each data and combining the section names, and obtaining a certain standard output value using Feature Hashing, and a type classification module generating a learning model after learning the data set with a Deep Neural Network(DNN) algorithm using extracted features, and classifying files for each packer type using the learning model after extracting features for the files to be classified.

Abstract: Disclosed is an LPC residual signal encoding/decoding apparatus of an MDCT based unified voice and audio encoding device. The LPC residual signal encoding apparatus analyzes a property of an input signal, selects an encoding method of an LPC filtered signal, and encode the LPC residual signal based on one of a real filterbank, a complex filterbank, and an algebraic code excited linear prediction (ACELP).

Abstract: An audio signal encoding and decoding method using a learning model, a training method of the learning model, and an encoder and decoder that perform the method, are disclosed. The audio signal decoding method may include extracting a first residual signal and a first linear prediction coefficient by decoding a bitstream received from an encoder, generating a first audio signal from the first residual signal using the first linear prediction coefficient, generating a second linear prediction coefficients and a second residual signal from the first audio signal, obtaining a third linear prediction coefficient by inputting the second linear prediction coefficient into a trained learning model, and generating a second audio signal from the second residual signal using the third linear prediction coefficient.

Abstract: Provided is a method and system for group-based energy harvesting. The group-based energy harvesting method performed by an access point in an energy harvesting system includes allocating an access period in each group including at least one station, receiving an energy state from the station in the group corresponding to the allocated access period, and scheduling stations in each group as a data transmission station or an energy reception station based on the received energy state.

Abstract: The present disclosure relates to a method and apparatus for selective communication between a tag and a reader using a filter. According to an embodiment of the present disclosure, a communication method between a tag and a reader using a filter performed by a reader includes generating a filter based on tag information of the tag to collect data, transmitting the generated filter to the tag, and receiving data from a tag that selected through a filtering operation of the transmitted filter.

Abstract: Methods of encoding and decoding an audio signal using a learning model and an encoder and a decoder for performing the methods are disclosed. A method of encoding an audio signal using a learning model may include extracting pitch information of the audio signal, determining a dilation factor of a receptive field of a first expandable neural network block to extract a feature map from the audio signal based on the pitch information, generating a first feature map of the audio signal using the first expandable neural network block in which the dilation factor is determined, determining a second feature map by inputting the first feature map into a second expandable neural network block to process the first feature map, and converting the second feature map and the pitch information into a bitstream.

Abstract: The present disclosure relates to an organic electroluminescent device comprising a light-emitting layer and a hole transport zone. By comprising a specific combination of a light-emitting layer and a hole transport zone, it is possible to provide an organic electroluminescent device having low driving voltage, high luminous efficiency and/or long lifespan properties.

Abstract: Disclosed are a training method for a learning model for recognizing an acoustic signal, a method of recognizing an acoustic signal using the learning model, and devices for performing the methods. The method of recognizing an acoustic signal using a learning model includes identifying an acoustic signal including an acoustic event or acoustic scene, determining an acoustic feature of the acoustic signal, dividing the determined acoustic feature for each of a plurality of frequency band intervals, and determining the acoustic event or acoustic scene included in the acoustic signal by inputting the divided acoustic features to a trained learning model.

Abstract: The present research relates to controlling rendering of multi-object or multi-channel audio signals. The present research provides a method and apparatus for controlling rendering of multi-object or multi-channel audio signals based on spatial cues in a process of decoding the multi-object or multi-channel audio signals. To achieve the purpose, the method suggested in the research controls rendering in a spatial cue domain in the process of decoding the multi-object or multi-channel audio signals.

Abstract: Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding using prediction. Multiple candidate prediction images are derived, and a final prediction image is generated using the multiple prediction images. The multiple candidate prediction images may be respectively generated using different methods. The multiple candidate prediction images may be generated using neural networks. Here, the multiple candidate prediction images may be respectively derived using different values for a specific coding parameter. Multiple neural networks may use different values for the specific coding parameter. Various coding parameters related to image encoding and/or decoding may be used for embodiments.